Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Signature recognition algorithms. bezier algorithm

Abstract: This article focuses on improving the human and machine interface, which should ensure efficient processing of data and knowledge in simple, fast and accessible ways. One of the ways to organize it is the introduction of the manuscript (entering text, drawings, drawings, etc.). Handwritten signatures can be considered as handwritten words, but they are more suitable for drawings, because the signer tries to make his signature unique, using not only his first and last names, but also additional graphic elements. Creating a signature is quite simple, although it is impossible to reproduce the recording speed. The signature has long been used to certify the authenticity of documents and verify (authenticate) an individual. In principle, the signature examination is used during the forensic examination. Signature recognition can be carried out by sequential verification of the signature to each known person. The signature recognition methodology includes a verification methodology and processing of verification results. One of the modern areas of interface improvement is the development and research of software for signature recognition and visualization. The advent of modern computer input tools has led to the emergence of a new type of online signature describing the signature creation process, not the result. Moreover, not only the coordinates of points on the line, but also a sequence of vectors of parameter values for each of the values of pressure, direction and speed of movement, the angle of adaptation of the pen and the signature time.

Applying the Aurora Feature Extraction Schemes to a Phoneme Based Recognition Task

Abstract: The robustness of the ETSI (European Telecommunication Standards Institute) standardized feature extraction schemes is investigated for phoneme based recognition tasks of German speech data. The recognition tasks are an isolated command word recognition and the recognition of connected digits. The motivation of this work is the easy extensibility of a whole word recognition system by allowing also the recognition of phoneme based word HMMs (Hidden Markov Models). The recognition performance has been determined for different numbers of HMM states and different numbers of Gaussians per state. It turns out that fairly high recognition rates can be achieved also for noisy data when applying the second robust ETSI frontend.

Efficient and Robust Optical Character Recognition Algorithm for Signature Recognition

Abstract: With the technology development over the past decades, it became necessary to provide secure recognition systems. The Optical Character Recognition (OCR) can be considered as one of the most useful software to offer security. It works on the principal of recognizing the patterns with the use of a computer algorithm. OCR has multiple uses in places that need security verification such as banks, elevators, police departments. Furthermore, it can be used in several categories simultaneously. There are two types of recognition. First is the static approach which is based on the information of the input. Second is the dynamic recognition which is more usable for recognition of speech. In fact, OCR will be one of the most important techniques for human computer interaction in future. However, in this paper we have used OCR as feature to implement our algorithm. We are presenting a new algorithm that is capable of recognizing each signature individually. This makes the system more efficient and robust,especially in banks which need to verify the customer’s signature on a regular basis. A highly efficient C# system was developed to implement the new algorithm.

A pattern matching approach to connected-word recognition for Chinese

Abstract: A speaker-dependent pattern-matching approach to connected word recognition for Chinese is presented. First, a method of adaptive energy normalization is applied to the speech spectrum, and a sound stimulus parameter is used to compress the normalized spectrum. Then, using a set of isolated word tokens as the reference patterns, a simplified dynamic programming-based matching strategy using a fast dynamic-time-warping alignment procedure is described. Experimental results for the recognition of a Chinese digit string (of unknown variable length from 2 to 5 digits), for two kinds of pronunciation (in standard Chinese and in the way used in telecommunication), are given. The correct string recognition rate is 96.8% and 97.6%, respectively. >

Reconocimiento de firmas off-line mediante máquinas de vectores de soporte Off-line signature recognition using support vector machines

Abstract: This paper presents two approaches for static signature recognition using Support Vector Machines (SVM): pure SVM and SVM integrated with a multilayer perceptron Artificial Neural Network (SVM/ANN) to map the results of the SVM. Feature extraction as a pattern construction method was adopted. Best results were obtained with the 1-v-r multivaluated classification method. The rate of correctly identified signers was similar for both architectures. A practical advantage of SVM/ANN architecture was decreasing the error of confusing the actual signer with another one: when the model misclassified a signer, instead of classifying it as a wrong signer, the proposed architecture recognized it as unknown.